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Refinement of a fuzzy control rule set
dc.contributor.author | González Muñoz, Antonio |
dc.contributor.author | Pérez Rodríguez, Raúl |
dc.date.accessioned | 2007-09-19T10:54:33Z |
dc.date.available | 2007-09-19T10:54:33Z |
dc.date.issued | 1998 |
dc.identifier.issn | 1134-5632 |
dc.identifier.uri | http://hdl.handle.net/2099/3514 |
dc.description.abstract | Fuzzy logic controller performance depends on the fuzzy control rule set. This set can be obtained either by an expert or from a learning algorithm through a set of examples. Recently, we have developed SLAVE an inductive learning algorithm capable of identifying fuzzy systems. The refinement of the rules proposed by SLAVE (or by an expert) can be very important in order to improve the accuracy of the model and in order to simplify the description of the system. The refinement algorithm is based on an heuristic process of generalization, specification, addition and elimination of rules. |
dc.format.extent | 175-187 |
dc.language.iso | eng |
dc.publisher | Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica |
dc.relation.ispartof | Mathware & soft computing . 1998 Vol. 5 Núm. 2 [ -3 ] |
dc.rights | Reconeixement-NoComercial-CompartirIgual 3.0 Espanya |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject.other | Theory refinemrnt |
dc.subject.other | Fuzzy logic |
dc.subject.other | Machine learning |
dc.subject.other | System modelling |
dc.subject.other | SLAVE |
dc.subject.other | Inductive learning algorithm |
dc.title | Refinement of a fuzzy control rule set |
dc.type | Article |
dc.subject.lemac | Programació (Matemàtica) |
dc.subject.lemac | Programació lògica |
dc.subject.ams | Classificació AMS::90 Operations research, mathematical programming::90C Mathematical programming |
dc.rights.access | Open Access |
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Aquest ítem apareix a les col·leccions següents
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1998, Vol. V, Núm. 2-3 [21]
ESTYLF'96, ESTYLF'97